DREADDs PST2020

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last pdf update generated ->  29 mai, 2024 - 16:21:40

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Introduction

A behavioral study of foraging

The data were acquired by JS, CG, and CREW at Oxford univ and at Inserm Lyon. In Lyon, Donut was tested at U1028 and Homer and Dali at U1208. The animals were tested everyday with different versions of the apparatus. The testing was done in animal housing using the 1st design realized by JS:

On this board the 25 locations are numbered from 1 to 25 from the upper left corner to the lower right corner, going from left to right.

Data loading and formating

Data files include all data tested in monkeys in Oxford and in Lyon.

This Markdown deals mostly with data from HOMER and DONUT.¹

session types are initially “0” for control (transparent doors) and “1” TEST with opaque opaque doors, but we use the label Clear vs opaque in figures and analyses. Codes used for target chosen and repeats or miss reflect the position of the choice (location of the hole selected from 1 TO 25 top to bottom) , if negative value then it’s a repeat, if 999 then it’s a pause in the task, if it’s a value between 100 and 2500 then it is a mistake (the animal tried but missed the reward) the number /100 giving the location.

!!!! ATTENTION : new data used on the 4.1.2024: there was a problem in the list of session for Homer, previous analyses for that monkey must not be considered.!!!!

Main general Plots

Let first see descriptive graphs for all sessions per monkey. The figures show for each monkey the choices (location of choice on board) selected by the animal trial by trial (chronological order from left to right). Green dots represent correct choices i.e. location chosen for the 1st time in session and with correct pick up of the reward. Blue dots represent returns to previously chosen location. These are presented as negative values to show their time course independently for correct choices. Orange is when reward is missed. Clear (transparent doors) and opaque (opaque doors) sessions are presented separately First sample sessions:

General figure HOMER

General figure HOMER

Then all data overlap across sessions to see the tendencies. In particular one can see the positive and negative trends that reflect monkeys choosing holes from top to bottom or bottom to top.Homer and Donut have different prefered directions but this is due to the different position of the setup in the housing.

General figure DONUT

General figure DONUT

Summary

The data are then summarized in terms of frequency of each trial (choice) type: Correct, Miss, Repeat.

Summary all trials

Below the average number of each trial type for the different ‘portes’ (door) conditions.

We do not look at injections yet, this will be done later in the statistical anayses (DCZ vs Sham). See for all 8 monkeys or for Donut and Homer that there is a main effect of doors in particular on the number of repeats. Which makes sense because monkeys in blue condition (compared to clear) monkeys need to rely on memory to avoid repeating ; which obviously they don’t really succeed. We see later the #repeat is a very relevant parameter.

##Stats We perform a logistic regression on the door effect for each monkey separately and test whether it influences the number of trial types - Still excluding DCZ sessions

## Warning: Dans subset.data.frame(agg.data4BnoDCZ, singe = "Homer") :
## l'argument supplémentaire 'singe' sera ignoré

## Warning: Dans subset.data.frame(agg.data4BnoDCZ, singe = "Homer") :
## l'argument supplémentaire 'singe' sera ignoré

## 
## Call:
## glm(formula = trial ~ choice_type * portes, family = "poisson", 
##     data = subset(agg.data4BnoDCZ, singe = "Homer"))
## 
## Coefficients:
##                                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                     3.10009    0.02632 117.763  < 2e-16 ***
## choice_typeMiss                -1.78526    0.07296 -24.470  < 2e-16 ***
## choice_typeRepeat              -1.21555    0.08380 -14.506  < 2e-16 ***
## portesopaque                   -0.05450    0.03237  -1.684   0.0922 .  
## choice_typeMiss:portesopaque    0.53014    0.08349   6.349 2.16e-10 ***
## choice_typeRepeat:portesopaque  1.73084    0.08712  19.868  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 7775  on 543  degrees of freedom
## Residual deviance: 3343  on 538  degrees of freedom
## AIC: 5688.3
## 
## Number of Fisher Scoring iterations: 6

## Warning: Dans subset.data.frame(agg.data4BnoDCZ, singe = "Donut") :
## l'argument supplémentaire 'singe' sera ignoré

## Warning: Dans subset.data.frame(agg.data4BnoDCZ, singe = "Donut") :
## l'argument supplémentaire 'singe' sera ignoré

## 
## Call:
## glm(formula = trial ~ choice_type * portes, family = "poisson", 
##     data = subset(agg.data4BnoDCZ, singe = "Donut"))
## 
## Coefficients:
##                                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                     3.10009    0.02632 117.763  < 2e-16 ***
## choice_typeMiss                -1.78526    0.07296 -24.470  < 2e-16 ***
## choice_typeRepeat              -1.21555    0.08380 -14.506  < 2e-16 ***
## portesopaque                   -0.05450    0.03237  -1.684   0.0922 .  
## choice_typeMiss:portesopaque    0.53014    0.08349   6.349 2.16e-10 ***
## choice_typeRepeat:portesopaque  1.73084    0.08712  19.868  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 7775  on 543  degrees of freedom
## Residual deviance: 3343  on 538  degrees of freedom
## AIC: 5688.3
## 
## Number of Fisher Scoring iterations: 6

##Exploration strategies

One future interesting set of analyses we can do regards the strategies of exploration: how animals scan through the setup , and then of course how they forget and repeat choices. Needs to be quantified to be used when comparing ON/OFF DCZ sessions.

One thing we can look at is the spatial variance between successive choices (here I do not differentiate Miss, Correct or repeat).

The figures below show the distributions of euclidean distances between 2 successive choices. The absolute distance between 2 holes (vertically and horizontally is 6.5cm). So we can see harmonics at 6.5 cm approximately in the distributions. Something quite obvious is that the harmonic is stronger in TEST than in CONTROL.

Note that here every choice is counted even repeats.

//////////// ATTENTION: here we will select only sessions that are labelled no, sham or DCZ (i.e. >=23 for Homer and >=24 for Donut ) ///////////

The distribution of distances between choices is of a particular form, somewhat LogNormal. We can look at this distributions depending on conditions and also compare with a random sampling of distances. Let’s first look at this accross the 2 monkeys.

The red shows distributions for monkeys, and blue shows a random sampling of 10000 distances.

Seperated for the 2 animals for Clear and opaque conditions we can see (below) that the distributions and the oscillation effects are stronger on opaque compared to Clear.

Spatial strategy. Distributions of euclidean distances

We test the difference in distributions between clear and blue for the 2 animals separately - we exclude DCZ sessions:

## 
##  Asymptotic two-sample Kolmogorov-Smirnov test
## 
## data:  data4B$distloc[data4B$singe == "Homer" & data4B$portes == "clear" & data4B$Injection != "DCZ"] and data4B$distloc[data4B$singe == "Homer" & data4B$portes == "opaque" & data4B$Injection != "DCZ"]
## D = 0.12461, p-value = 0.01327
## alternative hypothesis: two-sided

## 
##  Asymptotic two-sample Kolmogorov-Smirnov test
## 
## data:  data4B$distloc[data4B$singe == "Donut" & data4B$portes == "clear" & data4B$Injection != "DCZ"] and data4B$distloc[data4B$singe == "Donut" & data4B$portes == "opaque" & data4B$Injection != "DCZ"]
## D = 0.23894, p-value < 2.2e-16
## alternative hypothesis: two-sided

The KS tests indicate a difference between the 2 distributions (Clear and opaque) for both monkeys.

The strength of the ‘harmonic’ could be a marker of the two strategies used in the different sessions. The heavier harmonics in TEST could reflect an increased number of jumps between distant targets, whereas in CONTROL the animal would be more attracted to the visible reward which is just closeby to the current choice, hence proportionally more cases in which the animal choose the target just next the current one (6.5 cm distance). But we would need more control sessions to be sure.

Subset of trials

One observation is that monkeys often behave in a somewhat more controlled, organized, manner at the beginning of a session and then choices become more dispersed. This could correlate approximately with the completion of the task (i.e. having gone through all locations). So here we seperate the first 25 from the oher trials in 2 subset (25 corresponding to the number of locations on the setup).

Again here we remove DCZ (DCZ is taken into account in statistical analyses below)

There is obviously a lot of repeats (checks?) after 25 when the animals continue trying to get rewards, and of course especially in the opaque sessions. And Donut is a particularly good checker…

##The summary:

Below the average number of each trial type for the different conditions of injection (here again for the first 25 trials) :

Injection type 25 trials

##Spatial strategy:

Spatial organization in 2D space

The tendencies for choosing some locations rather than others can reveal spatial biases. SO we use here a 2D density mapping of choices to look at that.

Let’s look also at the patterns of choices separating between before and after 25:

STATISTICAL ANALYSES on DCZ vs Sham sessions

(Note we have no injection pre-surgery )

Here are the analyses and description of data for the 2 main types of sessions used on DCZ conditions. We subset the data for just the 2 monkeys and the 2 session types with an injection (sham and DCZ). We will also go through some more measures:

• test the numbers of repeats, and length distribution of distance to repeat
• test the number of pauses (code 99)

ATTENTION we remove the CONTROL sessions!!!!

## , ,  = Homer
## 
##         
##          sham DCZ
##   clear     6   8
##   opaque    7   9
## 
## , ,  = Donut
## 
##         
##          sham DCZ
##   clear     7  10
##   opaque   10  11

##Descriptions of sessions

Here we answer a few general questions on the sessions, choices, repeats etc..

First, were there more trials (choices+misses+repeats) in DCZ compared to sham sessions?

Summary 25 trials

Summary 25 trials

## 
## Call:
## glm(formula = trial ~ Injection, family = "poisson", data = subset(agg.data4B1, 
##     singe == "Homer"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   3.11180    0.05852  53.175  < 2e-16 ***
## InjectionDCZ  0.31647    0.07303   4.334 1.47e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 62.021  on 29  degrees of freedom
## Residual deviance: 42.742  on 28  degrees of freedom
## AIC: 200.13
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ Injection * portes, family = "poisson", 
##     data = subset(agg.data4B1, singe == "Homer"))
## 
## Coefficients:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                3.07577    0.08771  35.069   <2e-16 ***
## InjectionDCZ               0.17750    0.11191   1.586    0.113    
## portesopaque               0.06591    0.11775   0.560    0.576    
## InjectionDCZ:portesopaque  0.24249    0.14782   1.640    0.101    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 62.021  on 29  degrees of freedom
## Residual deviance: 30.303  on 26  degrees of freedom
## AIC: 191.69
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ Injection, family = "poisson", data = subset(agg.data4B1, 
##     singe == "Homer" & portes == "opaque"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   3.14169    0.07857  39.987  < 2e-16 ***
## InjectionDCZ  0.41999    0.09658   4.349 1.37e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 43.391  on 15  degrees of freedom
## Residual deviance: 23.730  on 14  degrees of freedom
## AIC: 110.95
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ Injection, family = "poisson", data = subset(agg.data4B1, 
##     singe == "Homer" & portes == "clear"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   3.07577    0.08771  35.069   <2e-16 ***
## InjectionDCZ  0.17750    0.11191   1.586    0.113    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 9.1199  on 13  degrees of freedom
## Residual deviance: 6.5729  on 12  degrees of freedom
## AIC: 80.747
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ Injection, family = "poisson", data = subset(agg.data4B1, 
##     singe == "Donut"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   3.97472    0.03324 119.573   <2e-16 ***
## InjectionDCZ  0.06330    0.04410   1.436    0.151    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 512.47  on 37  degrees of freedom
## Residual deviance: 510.40  on 36  degrees of freedom
## AIC: 731.79
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ Injection * portes, family = "poisson", 
##     data = subset(agg.data4B1, singe == "Donut"))
## 
## Coefficients:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                3.35739    0.07053  47.599   <2e-16 ***
## InjectionDCZ              -0.01101    0.09218  -0.119    0.905    
## portesopaque               0.89680    0.07997  11.214   <2e-16 ***
## InjectionDCZ:portesopaque  0.16906    0.10497   1.611    0.107    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 512.465  on 37  degrees of freedom
## Residual deviance:  79.081  on 34  degrees of freedom
## AIC: 304.47
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ Injection, family = "poisson", data = subset(agg.data4B1, 
##     singe == "Donut" & portes == "opaque"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   4.25419    0.03769 112.877  < 2e-16 ***
## InjectionDCZ  0.15805    0.05023   3.147  0.00165 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 81.897  on 20  degrees of freedom
## Residual deviance: 71.940  on 19  degrees of freedom
## AIC: 205.15
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ Injection, family = "poisson", data = subset(agg.data4B1, 
##     singe == "Donut" & portes == "clear"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   3.35739    0.07053  47.599   <2e-16 ***
## InjectionDCZ -0.01101    0.09218  -0.119    0.905    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 7.1551  on 16  degrees of freedom
## Residual deviance: 7.1409  on 15  degrees of freedom
## AIC: 99.32
## 
## Number of Fisher Scoring iterations: 4

Summary 25 trials

Homer performs more trials in DCZ compared to sham (when all conditions re taken together); not Donut.

But Statistics on Opaque (opaque) doors only shows an increase of total number of trials in DCZ sessions compared to Sham for the two monkeys.

##Figure1A

ggplot(agg.data4B1, aes(x=as.factor(Injection), y=trial   , fill=Injection))+
    geom_boxplot()+
  geom_jitter(aes(color=Injection), width=0.1) +
    facet_grid(singe~portes)+
    scale_fill_manual(values=c("grey","chartreuse3","dodgerblue1"))+
    scale_color_manual(values=c("grey50","chartreuse4","dodgerblue1"))+
    ggtitle("Total number of trials in sessions")+
  theme(axis.text.x = element_text(color = "grey20", size = 12))+
  theme_classic()

---

Then we ask whether the last correct trial performed is later in DCZ than in sham: this would mean that monkeys have more problems, or take more time, to find all or the max of rewards.

## 
## Call:
## glm(formula = trial_nb ~ Injection * portes, family = "poisson", 
##     data = subset(agg.maxcor, singe == "Homer"))
## 
## Coefficients:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                3.12090    0.08575  36.396   <2e-16 ***
## InjectionDCZ               0.16096    0.10976   1.466   0.1425    
## portesopaque              -0.09007    0.11937  -0.755   0.4505    
## InjectionDCZ:portesopaque  0.29117    0.14952   1.947   0.0515 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 57.678  on 29  degrees of freedom
## Residual deviance: 33.086  on 26  degrees of freedom
## AIC: 193.39
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial_nb ~ Injection, family = "poisson", data = subset(agg.maxcor, 
##     singe == "Homer" & portes == "opaque"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   3.03082    0.08305  36.496  < 2e-16 ***
## InjectionDCZ  0.45212    0.10154   4.453 8.47e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 43.139  on 15  degrees of freedom
## Residual deviance: 22.429  on 14  degrees of freedom
## AIC: 108.14
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial_nb ~ Injection * portes, family = "poisson", 
##     data = subset(agg.maxcor, singe == "Donut"))
## 
## Coefficients:
##                            Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                3.357395   0.070535  47.599   <2e-16 ***
## InjectionDCZ              -0.003988   0.092041  -0.043   0.9654    
## portesopaque               0.733611   0.081531   8.998   <2e-16 ***
## InjectionDCZ:portesopaque  0.205929   0.106706   1.930   0.0536 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 387.321  on 37  degrees of freedom
## Residual deviance:  82.512  on 34  degrees of freedom
## AIC: 305.01
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial_nb ~ Injection, family = "poisson", data = subset(agg.maxcor, 
##     singe == "Donut" & portes == "opaque"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   4.09101    0.04089 100.042  < 2e-16 ***
## InjectionDCZ  0.20194    0.05399   3.741 0.000184 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 93.307  on 20  degrees of freedom
## Residual deviance: 79.199  on 19  degrees of freedom
## AIC: 209.38
## 
## Number of Fisher Scoring iterations: 4

Both monkeys show a longer time to get to their last reward in session especially in opaque condition (tendency 0.07) for Homer.

##Figure1B

ggplot(subset(agg.maxcor, portes=='opaque'), aes(x=Injection,y=trial_nb, fill=Injection))+
  geom_boxplot()+
   geom_jitter(aes(colour=Injection), size=2,width = 0.1)+
    facet_grid(.~singe)+
    scale_fill_manual(values=c("grey","chartreuse3","dodgerblue1"))+
  scale_colour_manual(values=c("grey40","chartreuse4","dodgerblue1"))+
    ggtitle("Last correct trial in sessions")+
  theme(axis.text.x = element_text(color = "grey20", size = 12))+
  theme_classic()

This might mean that trials are instead repeats although for Homer there are more trials in sessions overall. So see let’s do the same analysis for Repeats, and then analyze the Repeats altogether

In the first analysis we take only the “opaque” sessions because we have very few repeats in “clear”:

## 
## Call:
## glm(formula = trial_nb ~ Injection, family = "poisson", data = subset(agg.maxrpt, 
##     singe == "Homer"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   3.11668    0.07956   39.18  < 2e-16 ***
## InjectionDCZ  0.43229    0.09759    4.43 9.44e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 47.279  on 15  degrees of freedom
## Residual deviance: 26.844  on 14  degrees of freedom
## AIC: 113.71
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial_nb ~ Injection, family = "poisson", data = subset(agg.maxrpt, 
##     singe == "Donut"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   4.27667    0.03727 114.755  < 2e-16 ***
## InjectionDCZ  0.16170    0.04963   3.258  0.00112 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 89.841  on 20  degrees of freedom
## Residual deviance: 79.164  on 19  degrees of freedom
## AIC: 212.84
## 
## Number of Fisher Scoring iterations: 4

Main effect for both monkeys. THe last repeat trial is later in DCZ than sham. Which goes with OR is equivalent to the fact their are more trials made under DCZ.

Do monkeys (especially Homer) get more rewards in DCZ?

## 
## Call:
## glm(formula = trial ~ Injection * portes, family = "poisson", 
##     data = subset(agg.NBcor, singe == "Homer"))
## 
## Coefficients:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                2.98736    0.09167  32.588  < 2e-16 ***
## InjectionDCZ               0.13720    0.11789   1.164  0.24450    
## portesopaque              -0.38998    0.13799  -2.826  0.00471 ** 
## InjectionDCZ:portesopaque  0.09207    0.17639   0.522  0.60170    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 34.811  on 29  degrees of freedom
## Residual deviance: 15.022  on 26  degrees of freedom
## AIC: 164.55
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ Injection, family = "poisson", data = subset(agg.NBcor, 
##     singe == "Homer" & portes == "opaque"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    2.5974     0.1031  25.183   <2e-16 ***
## InjectionDCZ   0.2293     0.1312   1.747   0.0806 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 12.6390  on 15  degrees of freedom
## Residual deviance:  9.5368  on 14  degrees of freedom
## AIC: 86.437
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ Injection, family = "poisson", data = subset(agg.NBcor, 
##     singe == "Homer" & portes == "clear"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   2.98736    0.09167  32.588   <2e-16 ***
## InjectionDCZ  0.13720    0.11789   1.164    0.244    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 6.8520  on 13  degrees of freedom
## Residual deviance: 5.4856  on 12  degrees of freedom
## AIC: 78.117
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ Injection * portes, family = "poisson", 
##     data = subset(agg.NBcor, singe == "Donut"))
## 
## Coefficients:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                3.17805    0.07715  41.192   <2e-16 ***
## InjectionDCZ               0.03279    0.09992   0.328    0.743    
## portesopaque              -0.06899    0.10206  -0.676    0.499    
## InjectionDCZ:portesopaque  0.02860    0.13515   0.212    0.832    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 8.6493  on 37  degrees of freedom
## Residual deviance: 7.3892  on 34  degrees of freedom
## AIC: 205.64
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ Injection, family = "poisson", data = subset(agg.NBcor, 
##     singe == "Donut" & portes == "opaque"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   3.10906    0.06682  46.532   <2e-16 ***
## InjectionDCZ  0.06139    0.09100   0.675      0.5    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 6.9084  on 20  degrees of freedom
## Residual deviance: 6.4526  on 19  degrees of freedom
## AIC: 115
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ Injection, family = "poisson", data = subset(agg.NBcor, 
##     singe == "Donut" & portes == "clear"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   3.17805    0.07715  41.192   <2e-16 ***
## InjectionDCZ  0.03279    0.09992   0.328    0.743    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1.04449  on 16  degrees of freedom
## Residual deviance: 0.93658  on 15  degrees of freedom
## AIC: 90.631
## 
## Number of Fisher Scoring iterations: 3

There is a tendency for Homer to get more rewards in Opaque but not significant, and not for Donut. [[ stats complicated here as numbers saturate at 25)]]

Below we can graph the overall trends of choices across sessions. The lines represent the fit to choice patterns (as shown in the very first figures). Positive slopes means searching holes from top to bottom, and negative slopes from bottom to top. The length actually covers the number of trials.

There are changes regarding repeats but these are different for the 2 monkeys. Let’s test statistically the data for All trials, for <25 trials and for >25 trials:

Summary 25 trials

Summary 25 trials

Pause in sessions

Monkeys sometime break during the task for short (code 99: en general prise d’info sur les autres singes) ; le singe reste de face ou de profil par rapport à la PST ) or long (code 999: pause avec départ ; Le singe n’est plus en face du setup ou/et s’engage dans une autre activité (fouragement, jeu avec les chaines ou les mousquetons du setup, boit, remonte sur son perchoir) ) pauses.

THere is an interest in looking at the number and distribution of these pauses, as they may reflect laps/focus of attention, distractibility, lack/increase of motivation, etc.

## 
## Call:
## glm(formula = choice ~ Injection, family = "poisson", data = subset(shortp, 
##     singe == "Homer"))
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)
## (Intercept)     0.4055     0.2582   1.570    0.116
## Injectionsham   0.2877     0.3873   0.743    0.458
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 7.2471  on 15  degrees of freedom
## Residual deviance: 6.7030  on 14  degrees of freedom
## AIC: 48.48
## 
## Number of Fisher Scoring iterations: 5

## 
## Call:
## glm(formula = choice ~ Injection, family = "poisson", data = subset(shortp, 
##     singe == "Donut"))
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)     1.0116     0.1741   5.811  6.2e-09 ***
## Injectionsham  -0.2167     0.2501  -0.866    0.386    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 19.586  on 25  degrees of freedom
## Residual deviance: 18.835  on 24  degrees of freedom
## AIC: 92.272
## 
## Number of Fisher Scoring iterations: 4

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: trial_nb ~ Injection + (1 | singe)
##    Data: short.pause
## 
##      AIC      BIC   logLik deviance df.resid 
##   1660.9   1668.4   -827.4   1654.9       88 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.4590 -2.9481  0.5138  2.3374  7.0528 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  singe  (Intercept) 0.306    0.5532  
## Number of obs: 91, groups:  singe, 2
## 
## Fixed effects:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    3.48352    0.39220   8.882  < 2e-16 ***
## Injectionsham -0.19059    0.03282  -5.807 6.34e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## Injectinshm -0.035

## 
## Call:
## glm(formula = trial_nb ~ Injection, family = "poisson", data = subset(short.pause, 
##     singe == "Homer"))
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    2.90872    0.06030  48.236   <2e-16 ***
## Injectionsham -0.14135    0.09419  -1.501    0.133    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 191.63  on 26  degrees of freedom
## Residual deviance: 189.36  on 25  degrees of freedom
## AIC: 313.22
## 
## Number of Fisher Scoring iterations: 5

## 
## Call:
## glm(formula = trial_nb ~ Injection, family = "poisson", data = subset(short.pause, 
##     singe == "Donut"))
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    4.03932    0.02310  174.86  < 2e-16 ***
## Injectionsham -0.19745    0.03501   -5.64  1.7e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1005.86  on 63  degrees of freedom
## Residual deviance:  973.83  on 62  degrees of freedom
## AIC: 1335.4
## 
## Number of Fisher Scoring iterations: 5

Statistics show that across monkeys pauses are made earlier in sham compared to DCZ. (significant for both monkeys together or individually)

##Figure2A

cc <- ggplot(shortp, aes(x=Injection, y=choice, fill=Injection))+
    geom_boxplot()+
  geom_jitter(aes(colour=Injection),size= 2, width=.1)+
    facet_grid(. ~ singe)+
    scale_fill_manual(values=c("chartreuse3","grey"))+
   scale_colour_manual(values=c("chartreuse4","grey60"))+
    ggtitle("Number of SHORT pauses during sessions")+
  theme(title =element_text(size=8, face='bold'),axis.text.x = element_text(color = "grey20", size = 12))+
  ylim(0,8) + theme_classic() +
  theme(legend.position="none")



bb <- ggplot(short.pause, aes(x= Injection, y=trial_nb, fill=Injection))+
    geom_boxplot(aes(colour=Injection))+coord_flip()+
      geom_jitter(aes(colour=Injection), width=0.1)+
    facet_grid(singe ~ .)+
    scale_fill_manual(values=c("chartreuse3","grey"))+
   scale_colour_manual(values=c("chartreuse4","grey60"))+
    ggtitle("Distribution of short pauses ")+
  theme(title =element_text(size=9, face='bold'), axis.text.x = element_text(color = "grey20", size = 10)) + theme_classic()


grid.arrange(cc, bb, ncol=2)

Trial types:

let’s look at the frequency of repeats, miss, etc…

## 
## Call:
## glm(formula = trial ~ choice_type/Injection, family = "poisson", 
##     data = subset(agg.data4B, singe == "Homer" & portes == "clear"))
## 
## Coefficients:
##                                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                      2.98736    0.09167  32.588  < 2e-16 ***
## choice_typeMiss                 -2.47654    0.45651  -5.425 5.80e-08 ***
## choice_typeRepeat               -2.29422    0.41841  -5.483 4.18e-08 ***
## choice_typeCorrect:InjectionDCZ  0.13720    0.11789   1.164   0.2445    
## choice_typeMiss:InjectionDCZ     0.35417    0.50262   0.705   0.4810    
## choice_typeRepeat:InjectionDCZ   1.09861    0.57735   1.903   0.0571 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 274.647  on 28  degrees of freedom
## Residual deviance:  11.695  on 23  degrees of freedom
## AIC: 132.12
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ choice_type/Injection, family = "poisson", 
##     data = subset(agg.data4B, singe == "Homer" & portes == "opaque"))
## 
## Coefficients:
##                                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                      2.59738    0.10314  25.183  < 2e-16 ***
## choice_typeMiss                 -1.43423    0.27044  -5.303 1.14e-07 ***
## choice_typeRepeat               -0.59205    0.17283  -3.426 0.000613 ***
## choice_typeCorrect:InjectionDCZ  0.22927    0.13121   1.747 0.080585 .  
## choice_typeMiss:InjectionDCZ     0.08961    0.31339   0.286 0.774921    
## choice_typeRepeat:InjectionDCZ   0.71742    0.16288   4.405 1.06e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 183.374  on 44  degrees of freedom
## Residual deviance:  39.504  on 39  degrees of freedom
## AIC: 231.19
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ choice_type/Injection, family = "poisson", 
##     data = subset(agg.data4B, singe == "Donut" & portes == "clear"))
## 
## Coefficients:
##                                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                      3.17805    0.07715  41.192  < 2e-16 ***
## choice_typeMiss                 -1.52940    0.21075  -7.257 3.96e-13 ***
## choice_typeRepeat               -2.61844    0.38576  -6.788 1.14e-11 ***
## choice_typeCorrect:InjectionDCZ  0.03279    0.09992   0.328    0.743    
## choice_typeMiss:InjectionDCZ    -0.41190    0.26593  -1.549    0.121    
## choice_typeRepeat:InjectionDCZ   1.04982    0.58554   1.793    0.073 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 332.6456  on 35  degrees of freedom
## Residual deviance:   8.5136  on 30  degrees of freedom
## AIC: 164.28
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ choice_type/Injection, family = "poisson", 
##     data = subset(agg.data4B, singe == "Donut" & portes == "opaque"))
## 
## Coefficients:
##                                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                      3.10906    0.06682  46.532  < 2e-16 ***
## choice_typeMiss                 -0.58333    0.11164  -5.225 1.74e-07 ***
## choice_typeRepeat                0.46047    0.08533   5.396 6.80e-08 ***
## choice_typeCorrect:InjectionDCZ  0.06139    0.09100   0.675    0.500    
## choice_typeMiss:InjectionDCZ    -0.18743    0.12950  -1.447    0.148    
## choice_typeRepeat:InjectionDCZ   0.30733    0.06856   4.483 7.36e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 621.09  on 62  degrees of freedom
## Residual deviance: 194.79  on 57  degrees of freedom
## AIC: 512.65
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ choice_type/Injection, family = "poisson", 
##     data = subset(agg.data4B.first30, singe == "Homer" & portes == 
##         "clear"))
## 
## Coefficients:
##                                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                      2.98736    0.09167  32.588  < 2e-16 ***
## choice_typeMiss                 -2.47654    0.45651  -5.425 5.80e-08 ***
## choice_typeRepeat               -2.29422    0.41841  -5.483 4.18e-08 ***
## choice_typeCorrect:InjectionDCZ  0.12615    0.11815   1.068    0.286    
## choice_typeMiss:InjectionDCZ     0.35417    0.50262   0.705    0.481    
## choice_typeRepeat:InjectionDCZ   0.91629    0.60553   1.513    0.130    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 273.22  on 28  degrees of freedom
## Residual deviance:  11.46  on 23  degrees of freedom
## AIC: 131.63
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ choice_type/Injection, family = "poisson", 
##     data = subset(agg.data4B.first30, singe == "Donut" & portes == 
##         "clear"))
## 
## Coefficients: (1 not defined because of singularities)
##                                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                      3.16004    0.07785  40.591  < 2e-16 ***
## choice_typeMiss                 -1.51138    0.21100  -7.163  7.9e-13 ***
## choice_typeRepeat               -2.46689    0.71138  -3.468 0.000525 ***
## choice_typeCorrect:InjectionDCZ  0.05081    0.10046   0.506 0.613039    
## choice_typeMiss:InjectionDCZ    -0.41190    0.26593  -1.549 0.121411    
## choice_typeRepeat:InjectionDCZ        NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 267.0122  on 31  degrees of freedom
## Residual deviance:   6.9735  on 27  degrees of freedom
## AIC: 150.15
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ choice_type/Injection, family = "poisson", 
##     data = subset(agg.data4B.first30, singe == "Homer" & portes == 
##         "opaque"))
## 
## Coefficients:
##                                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                      2.59738    0.10314  25.183  < 2e-16 ***
## choice_typeMiss                 -1.43423    0.27044  -5.303 1.14e-07 ***
## choice_typeRepeat               -0.59205    0.17283  -3.426 0.000613 ***
## choice_typeCorrect:InjectionDCZ  0.13986    0.13354   1.047 0.294927    
## choice_typeMiss:InjectionDCZ    -0.06454    0.32275  -0.200 0.841507    
## choice_typeRepeat:InjectionDCZ   0.37215    0.17187   2.165 0.030366 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 136.831  on 44  degrees of freedom
## Residual deviance:  16.988  on 39  degrees of freedom
## AIC: 204.28
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = trial ~ choice_type/Injection, family = "poisson", 
##     data = subset(agg.data4B.first30, singe == "Donut" & portes == 
##         "opaque"))
## 
## Coefficients:
##                                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                      2.63189    0.08482  31.030  < 2e-16 ***
## choice_typeMiss                 -0.26165    0.12861  -2.034   0.0419 *  
## choice_typeRepeat               -0.98323    0.16256  -6.049 1.46e-09 ***
## choice_typeCorrect:InjectionDCZ  0.12351    0.11391   1.084   0.2782    
## choice_typeMiss:InjectionDCZ    -0.32549    0.14529  -2.240   0.0251 *  
## choice_typeRepeat:InjectionDCZ   0.20194    0.18307   1.103   0.2700    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 156.860  on 62  degrees of freedom
## Residual deviance:  61.304  on 57  degrees of freedom
## AIC: 325.89
## 
## Number of Fisher Scoring iterations: 4

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: trial ~ choice_type/Injection + (1 | singe)
##    Data: subset(agg.data4B.first30, portes == "opaque")
## 
##      AIC      BIC   logLik deviance df.resid 
##    581.4    600.2   -283.7    567.4      101 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5143 -0.8562 -0.0476  0.6219  3.7715 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  singe  (Intercept) 0        0       
## Number of obs: 108, groups:  singe, 2
## 
## Fixed effects:
##                                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                      2.61783    0.06551  39.959  < 2e-16 ***
## choice_typeMiss                 -0.51369    0.11145  -4.609 4.05e-06 ***
## choice_typeRepeat               -0.80665    0.11793  -6.840 7.91e-12 ***
## choice_typeCorrect:InjectionDCZ  0.12945    0.08659   1.495  0.13491    
## choice_typeMiss:InjectionDCZ    -0.35723    0.13155  -2.716  0.00662 ** 
## choice_typeRepeat:InjectionDCZ   0.31108    0.12491   2.490  0.01276 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) chc_tM chc_tR c_C:ID c_M:ID
## choc_typMss -0.588                            
## choc_typRpt -0.556  0.327                     
## chc_tC:IDCZ -0.757  0.445  0.420              
## chc_tM:IDCZ  0.000 -0.555  0.000  0.000       
## chc_tR:IDCZ  0.000  0.000 -0.653  0.000  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

The stats shows that when All trials are taken into account , both animals made more REpeats in DCZ than in Sham. (This is probably linked to the increased number of trials).

We analyzed the 30 first trials to check whether changes in the strategy or efficiency of the animals could be detected.THe average number of Miss is reduced in bpth monkeys but it is significant (with the full model) only for Donut. In the mixed model with both monkeys (Random factor) we find increased number of REPEATS and decreased number of MISS

##XYplot trial types

## 
## Call:
## glm(formula = Repeat_trials ~ Correct_trials * Injection, data = subset(xyplot_data.first30, 
##     singe == "Homer"))
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                   9.6250     2.6509   3.631  0.00178 **
## Correct_trials               -0.2500     0.2018  -1.239  0.23057   
## InjectionDCZ                  8.0777     3.2681   2.472  0.02307 * 
## Correct_trials:InjectionDCZ  -0.2010     0.2354  -0.854  0.40384   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 1.140645)
## 
##     Null deviance: 135.304  on 22  degrees of freedom
## Residual deviance:  21.672  on 19  degrees of freedom
## AIC: 73.904
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = Repeat_trials ~ Correct_trials * Injection, data = subset(xyplot_data.first30, 
##     singe == "Donut"))
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                  -1.4741     2.7541  -0.535   0.5970  
## Correct_trials                0.4793     0.2020   2.372   0.0254 *
## InjectionDCZ                  8.1117     4.5915   1.767   0.0890 .
## Correct_trials:InjectionDCZ  -0.5196     0.3062  -1.697   0.1017  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 3.945685)
## 
##     Null deviance: 133.37  on 29  degrees of freedom
## Residual deviance: 102.59  on 26  degrees of freedom
## AIC: 132.02
## 
## Number of Fisher Scoring iterations: 2

The statistics show that there is no effect of the condition (Injection) for Homer but there is a significant increase of repeat for Donut. This is when we do not take PORTES into account. If Portes is used as an interacting fixed effect the effect size for Repeats in Donut goes down (p=0.052).

Cumsum Reward

Test whether the speed of getting rewards changes between conditions and under DCZ vs sham.

## 
##  Exact two-sample Kolmogorov-Smirnov test
## 
## data:  cum$cv[cum$portes == "opaque" & cum$singe == "Homer" & cum$Injection == "sham"] and cum$cv[cum$portes == "opaque" & cum$singe == "Homer" & cum$Injection == "DCZ"]
## D = 0.41818, p-value = 0.002047
## alternative hypothesis: two-sided

## 
##  Exact two-sample Kolmogorov-Smirnov test
## 
## data:  cum$cv[cum$portes == "opaque" & cum$singe == "Donut" & cum$Injection == "sham"] and cum$cv[cum$portes == "opaque" & cum$singe == "Donut" & cum$Injection == "DCZ"]
## D = 0.18313, p-value = 0.06072
## alternative hypothesis: two-sided

## 
##  Exact two-sample Kolmogorov-Smirnov test
## 
## data:  cum30$cv[cum30$portes == "opaque" & cum30$singe == "Homer" & cum30$Injection == "sham"] and cum30$cv[cum30$portes == "opaque" & cum30$singe == "Homer" & cum30$Injection == "DCZ"]
## D = 0.36143, p-value = 0.01305
## alternative hypothesis: two-sided

## 
##  Exact two-sample Kolmogorov-Smirnov test
## 
## data:  cum30$cv[cum30$portes == "opaque" & cum30$singe == "Donut" & cum30$Injection == "sham"] and cum30$cv[cum30$portes == "opaque" & cum30$singe == "Donut" & cum30$Injection == "DCZ"]
## D = 0.1, p-value = 0.966
## alternative hypothesis: two-sided

There is an impact of DCZ on the speed at which animals get rewards under DCZ compared to Sham, in the opaque condition, not in the clear. Specifically it seems that animals accumulate rewards faster under DCZ.IT is not significant if one takes only the first 50 trials for the 2 animals

One possibility is that it’s because animals make less repeats at the begining thus cumulating rewards faster.

Cumsum repeats

Test whether the repeats appear later using a cumsum curve

No different.

##stats spatial strategy

There is no difference in distribution of distances between sham and DCZ.

Clusters of small distances

We look at clustering in the sense of succession of choices at small distances.

## 
## Call:
## lm(formula = normdist ~ portes.x * Injection.x, data = subset(newdata2, 
##     singe.x == "Donut"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.80139 -0.14844  0.00233  0.18909  0.51287 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     1.5184     0.1198  12.671 1.97e-14 ***
## portes.xopaque                  0.1249     0.1562   0.799    0.430    
## Injection.xDCZ                  0.1497     0.1562   0.958    0.345    
## portes.xopaque:Injection.xDCZ  -0.1053     0.2088  -0.504    0.617    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.317 on 34 degrees of freedom
## Multiple R-squared:  0.03841,    Adjusted R-squared:  -0.04644 
## F-statistic: 0.4527 on 3 and 34 DF,  p-value: 0.7171

## 
## Call:
## lm(formula = normdist ~ portes.x * Injection.x, data = subset(newdata2, 
##     singe.x == "Homer"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.81273 -0.16082 -0.01963  0.12806  0.69782 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    1.58441    0.12304  12.877 8.64e-13 ***
## portes.xopaque                 0.14314    0.16768   0.854    0.401    
## Injection.xDCZ                -0.08747    0.16277  -0.537    0.596    
## portes.xopaque:Injection.xDCZ  0.06950    0.22263   0.312    0.757    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3014 on 26 degrees of freedom
## Multiple R-squared:  0.1052, Adjusted R-squared:  0.001928 
## F-statistic: 1.019 on 3 and 26 DF,  p-value: 0.4004

## 
## Call:
## glm(formula = clust ~ portes * Injection, family = "poisson", 
##     data = subset(distClustyClust.session, singe == "Donut"))
## 
## Coefficients:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                0.89948    0.24106   3.731  0.00019 ***
## portesopaque               0.27954    0.29811   0.938  0.34840    
## InjectionDCZ               0.07956    0.30932   0.257  0.79702    
## portesopaque:InjectionDCZ -0.09763    0.39358  -0.248  0.80409    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 5.2827  on 37  degrees of freedom
## Residual deviance: 3.8751  on 34  degrees of freedom
## AIC: Inf
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## glm(formula = clust ~ portes * Injection, family = "poisson", 
##     data = subset(distClustyClust.session, singe == "Homer"))
## 
## Coefficients:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                0.87547    0.26352   3.322 0.000893 ***
## portesopaque               0.13179    0.34874   0.378 0.705493    
## InjectionDCZ              -0.01223    0.34953  -0.035 0.972094    
## portesopaque:InjectionDCZ -0.11073    0.46929  -0.236 0.813465    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2.0457  on 29  degrees of freedom
## Residual deviance: 1.7967  on 26  degrees of freedom
## AIC: Inf
## 
## Number of Fisher Scoring iterations: 4

## 
## Call:
## lm(formula = normclust ~ portes.x * Injection.x, data = subset(newdata2, 
##     singe.x == "Donut"))
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.085162 -0.019141  0.002136  0.012457  0.129248 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    0.113176   0.016030   7.060 3.75e-08 ***
## portes.xopaque                 0.046286   0.020900   2.215   0.0336 *  
## Injection.xDCZ                 0.007701   0.020900   0.368   0.7148    
## portes.xopaque:Injection.xDCZ -0.007854   0.027932  -0.281   0.7803    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.04241 on 34 degrees of freedom
## Multiple R-squared:  0.2131, Adjusted R-squared:  0.1437 
## F-statistic: 3.069 on 3 and 34 DF,  p-value: 0.04085

## 
## Call:
## lm(formula = normclust ~ portes.x * Injection.x, data = subset(newdata2, 
##     singe.x == "Homer"))
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.127614 -0.037568 -0.000705  0.039737  0.105619 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    0.15299    0.02148   7.123 1.45e-07 ***
## portes.xopaque                 0.01176    0.02927   0.402    0.691    
## Injection.xDCZ                -0.03721    0.02841  -1.310    0.202    
## portes.xopaque:Injection.xDCZ  0.03711    0.03886   0.955    0.348    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.05261 on 26 degrees of freedom
## Multiple R-squared:  0.1516, Adjusted R-squared:  0.05371 
## F-statistic: 1.549 on 3 and 26 DF,  p-value: 0.2257

After counting and normalizing we don’t find any difference in the clustering of distances between choices: no difference in the number of choices Hence the strategy of the animal, in terms of clustering choices with small distances, don’t seem to differ.

Note that surprisingly only Donut shows the effect in which the number of cluster with short distances is larger in opaque than in Clear conditions. Homer does not seem to change his strategy in that regard between the 2…

However, this is surprising because analyses and figures above suggest that under rDCZ in opaque animals get rewards faster and that it could be accompanied by more short distances between 2 successive choices. !! NEED to investigate further!!

Cumsum distance - trajectory

Test whether the speed of getting rewards changes between conditions and under DCZ vs sham.

POST-OUTCOME reaction vs Reward

Test whether the choices made after no rewards (e.g. next distance of choice: close or far). We hypothesize that in the opaque condition (the animal doesn’t see rewards) the distance after negative outcomes (for a repeat) is larger than after a correct rewarded response, because when rewarded the animal will stay ‘in the patch’ i.e. close to where he got the reward.

We remove Clear conditions, and misses because it’s not appropriate.

## Linear mixed-effects model fit by REML
##   Data: subset(mean.nextdist, Injection == "sham") 
##        AIC      BIC    logLik
##   257.6596 265.5407 -124.8298
## 
## Random effects:
##  Formula: ~1 | singe
##          (Intercept) Residual
## StdDev: 9.748496e-05 2.396026
## 
## Fixed effects:  nextdist ~ choice_type 
##                      Value Std.Error DF   t-value p-value
## (Intercept)       9.526671 0.4611155 52 20.660054  0.0000
## choice_typeRepeat 1.721628 0.6462671 52  2.663957  0.0103
##  Correlation: 
##                   (Intr)
## choice_typeRepeat -0.714
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.9817389 -0.5213904 -0.1182803  0.4321110  3.4439110 
## 
## Number of Observations: 55
## Number of Groups: 2

##             numDF denDF   F-value p-value
## (Intercept)     1    52 1036.8311  <.0001
## choice_type     1    52    7.0967  0.0103

## Linear mixed-effects model fit by REML
##   Data: mean.nextdist 
##       AIC      BIC    logLik
##   648.313 665.4252 -318.1565
## 
## Random effects:
##  Formula: ~1 | singe
##         (Intercept) Residual
## StdDev:   0.6385261  2.73632
## 
## Fixed effects:  nextdist ~ Injection * choice_type 
##                                    Value Std.Error  DF   t-value p-value
## (Intercept)                     9.716682 0.7016593 127 13.848148  0.0000
## InjectionDCZ                    0.586587 0.6955482 127  0.843345  0.4006
## choice_typeRepeat               1.700747 0.7381438 127  2.304086  0.0228
## InjectionDCZ:choice_typeRepeat -0.215577 0.9666098 127 -0.223024  0.8239
##  Correlation: 
##                                (Intr) InjDCZ chc_tR
## InjectionDCZ                   -0.582              
## choice_typeRepeat              -0.538  0.542       
## InjectionDCZ:choice_typeRepeat  0.408 -0.713 -0.763
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.8612082 -0.6587507 -0.1052453  0.3905203  3.7617502 
## 
## Number of Observations: 132
## Number of Groups: 2

##                       numDF denDF  F-value p-value
## (Intercept)               1   127 448.6231  <.0001
## Injection                 1   127   0.9869  0.3224
## choice_type               1   127  10.9104  0.0012
## Injection:choice_type     1   127   0.0497  0.8239

## 
## Call:
## lm(formula = nextdist ~ Injection * choice_type, data = subset(mean.nextdist, 
##     singe == "Donut" & portes == "opaque"))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9170 -1.3114 -0.1794  0.8692 10.3125 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     9.32160    0.55640  16.753  < 2e-16 ***
## InjectionDCZ                   -0.08127    0.76878  -0.106  0.91607    
## choice_typeRepeat               2.09544    0.78687   2.663  0.00936 ** 
## InjectionDCZ:choice_typeRepeat  0.03893    1.08722   0.036  0.97153    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.488 on 80 degrees of freedom
## Multiple R-squared:  0.1596, Adjusted R-squared:  0.1281 
## F-statistic: 5.066 on 3 and 80 DF,  p-value: 0.002906

## 
## Call:
## lm(formula = nextdist ~ Injection * choice_type, data = subset(mean.nextdist, 
##     singe == "Homer" & portes == "opaque"))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1781 -2.3404 -0.7444  1.9236  8.8767 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     10.1126     1.1708   8.637    5e-11 ***
## InjectionDCZ                     1.5655     1.4179   1.104    0.276    
## choice_typeRepeat                0.7139     1.6032   0.445    0.658    
## InjectionDCZ:choice_typeRepeat  -0.1855     1.9347  -0.096    0.924    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.098 on 44 degrees of freedom
## Multiple R-squared:  0.05919,    Adjusted R-squared:  -0.004961 
## F-statistic: 0.9227 on 3 and 44 DF,  p-value: 0.4378

We observe a post-error effect: after a repeat (negative outcome) the next choice is further away than after a rewarded choice. The effect is however not significant for Homer. There is no DCZ effect in both.

##Figure2B

ggplot(mean.nextdist, aes(choice_type, y=nextdist, fill=choice_type))+
  geom_boxplot(outlier.shape = NA)+
  geom_point(aes(colour=Injection), position = position_jitterdodge())+
  facet_grid(singe~ Injection)+
  ylim(5,18)+
  ylab('Distance to next choice (cm)')+
  scale_fill_manual(values=c("grey30","grey60"))+
  scale_colour_manual(values=c("grey40","chartreuse3"))+
  ggtitle("Euclidian distance to the next choice \n after positive or negative outcomes")+
  theme_classic()

Other option is to check whether the probability to do a short vs. long shift depends on the previous reward, using logistic regressions.

Now how does this work with successive trials : does the cumulative outcome (e.g. average outcome in 5 last choices) impact the distance of leave after a negative outcome? The hypothesis is that there should be a threshold to “leave the patch” in terms of average reward encountered. We don’t look first at average values but at successions of rwd: ..010, .0110, 01110, 11110

## 
## Call:
## lm(formula = distNeg ~ Value * Injection, data = subset(MeanValueNeg.nextdist, 
##     singe == "Donut" & Feedback == "Negative"))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4737 -1.8890 -0.5073  1.3366 15.1996 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          9.9035     0.8081  12.256  < 2e-16 ***
## Value                2.5877     1.7672   1.464  0.14613    
## InjectionDCZ         1.9929     1.1164   1.785  0.07714 .  
## Value:InjectionDCZ  -6.9720     2.3866  -2.921  0.00428 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.323 on 104 degrees of freedom
## Multiple R-squared:  0.09425,    Adjusted R-squared:  0.06812 
## F-statistic: 3.607 on 3 and 104 DF,  p-value: 0.01586

## 
## Call:
## lm(formula = distNeg ~ Value * Injection, data = subset(MeanValueNeg.nextdist, 
##     singe == "Homer" & Feedback == "Negative"))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.7392 -2.7161 -0.8982  1.6590 20.5108 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          14.492      2.377   6.097  6.3e-08 ***
## Value                -7.211      4.229  -1.705   0.0929 .  
## InjectionDCZ         -3.241      2.800  -1.158   0.2512    
## Value:InjectionDCZ    8.133      5.130   1.585   0.1177    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.889 on 66 degrees of freedom
## Multiple R-squared:  0.05137,    Adjusted R-squared:  0.008255 
## F-statistic: 1.191 on 3 and 66 DF,  p-value: 0.3199

Distance to repeat

## 
## Call:
## glm(formula = d2rpt ~ Injection, family = "poisson", data = subset(stats.repeat, 
##     singe == "Homer" & portes == "opaque"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   2.06142    0.05096  40.448  < 2e-16 ***
## InjectionDCZ  0.23373    0.05782   4.042  5.3e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 844.63  on 183  degrees of freedom
## Residual deviance: 827.59  on 182  degrees of freedom
## AIC: 1532.8
## 
## Number of Fisher Scoring iterations: 5

## 
## Call:
## glm(formula = d2rpt ~ Injection, family = "poisson", data = subset(stats.repeat, 
##     singe == "Donut" & portes == "opaque"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   2.69952    0.01380 195.598  < 2e-16 ***
## InjectionDCZ  0.06016    0.01763   3.412 0.000645 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 5717.5  on 878  degrees of freedom
## Residual deviance: 5705.8  on 877  degrees of freedom
## AIC: 9535
## 
## Number of Fisher Scoring iterations: 5

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: d2rpt ~ Injection + (1 | singe)
##    Data: subset(stats.repeat, portes == "opaque")
## 
##      AIC      BIC   logLik deviance df.resid 
##  11087.8  11102.7  -5540.9  11081.8     1060 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.7344 -1.8032 -0.7234  1.2839 13.3588 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  singe  (Intercept) 0.06424  0.2535  
## Number of obs: 1063, groups:  singe, 2
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   2.43701    0.18005  13.535  < 2e-16 ***
## InjectionDCZ  0.07538    0.01683   4.479 7.49e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## InjectinDCZ -0.064

Regarding distances between a choice and its repeat, DCZ effects are present for both monkeys -> distances are longer under DCZ in opaque conditions.

##Figure1C

ggplot( data=subset(stats.repeat, portes=="opaque"), aes(x = d2rpt, color = Injection))+
  geom_density(aes(fill=Injection),size=1, alpha=.5)+
  facet_grid(singe~ portes)+
  geom_vline(data=medD2rpt, aes(xintercept = median, color=Injection), linetype="dashed", size=1)+
     scale_color_manual(values=c("grey40","chartreuse3","dodgerblue1"))+
     scale_fill_manual(values=c("grey40","chartreuse3","dodgerblue1"))+
  ggtitle('distance between a choice and its repetition')+
  theme_classic()

2D choices

Script below is to make moving graph of choices… for fun

---

1. The first descriptive analyses were in Graph_PST2020choices.R - now in PST2020_DREADDs.rmd]↩︎